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Abstract:

A plurality of sets of volume data, each of which represent the state of
a beating heart in different phases, are obtained. Coronary artery
regions are extracted from at least two sets of volume data from among
the obtained sets of volume data. A plurality of analysis points are set
in each extracted coronary artery region. Correlations are established
among analysis points set at the same anatomical positions within the
coronary artery regions. Index values that indicate the character of
plaque are calculated at each analysis point within all of the coronary
artery regions. The character of plaque is evaluated at positions within
the coronary artery regions, by integrating the index values calculated
at the analysis points corresponding to each of the positions. The
evaluation results regarding the character of plaque at each of the
positions within the coronary artery regions are output, correlated with
information regarding the positions.

Claims:

1. A diagnosis assisting apparatus, comprising: volume data obtaining
means, for obtaining a plurality of sets of volume data, each of which
represent the state of a beating heart in different phases; coronary
artery region extracting means, for extracting coronary artery regions
from at least two sets of volume data from among the obtained sets of
volume data; correlation establishing means, for setting a plurality of
analysis points in each of the extracted coronary artery regions, and for
establishing correlations among the analysis points, which are set at the
same anatomical positions, within the plurality of coronary artery
regions; index value calculating means, for calculating index values that
indicate the character of plaque at each of the analysis points within
all of the plurality of coronary artery regions; index value integrating
means, for evaluating the character of plaque at positions within the
coronary artery regions, by integrating the index values which are
calculated at the plurality of analysis points corresponding to each of
the positions; and output control means, for outputting the evaluation
results regarding the character of plaque at each of the positions within
the coronary artery regions, correlated with information regarding the
positions.

2. A diagnosis assisting apparatus as defined in claim 1, wherein: the
coronary artery region extracting means executes the process for
extracting the coronary artery regions at least with respect to a set of
volume data that represents the heart in a telesystolic state, and a set
of volume data that represents the heart in a middiastolic state.

3. A diagnosis assisting apparatus as defined in claim 1, wherein: the
index value integrating means calculates weighted averages of the
plurality of index values by multiplying the index values by weighting
coefficients which are set for each of the phases, and evaluates the
character of plaque based on the values of the weighted averages.

4. A diagnosis assisting apparatus as defined in claim 3, wherein: the
weighting coefficients that the index values calculated for analysis
points corresponding to positions within a right coronary artery region
are multiplied by are set higher for the telesystolic phase than for
other phases; and the weighting coefficients that the index values
calculated for analysis points corresponding to positions within a left
coronary artery region are multiplied by are set higher for the
middiastolic phase than for other phases.

5. A diagnosis assisting apparatus as defined in claim 1, wherein: the
index value calculating means calculates the index values based on at
least one of the diameter, the area, and the signal values at the
analysis points of at least one of the coronary artery region and the
intravascular regions of coronary arteries.

6. A diagnosis assisting apparatus as defined in claim 1, further
comprising: alert required region detecting means, for detecting alert
required regions based on the evaluation results regarding the character
of plaque; and wherein the output control means outputs the detected
alert required regions in a discernable manner during output of the
evaluation results.

7. A diagnosis assisting apparatus as defined in claim 6, wherein: the
alert required region detecting means detects regions having index values
that indicate at least one of a stenosis rate and instability of plaque
greater than a predetermined threshold value.

8. A diagnosis assisting apparatus as defined in claim 1, wherein: the
output control means displays the evaluation results such that they
overlap images that represent the coronary artery region.

9. A computer readable non transitory storage medium storing a coronary
artery analyzing program therein, the program, when executed on at least
one computer, causing the at least one computer to perform a coronary
artery analyzing method, comprising: obtaining a plurality of sets of
volume data, each of which represent the state of a beating heart in
different phases; extracting coronary artery regions from at least two
sets of volume data from among the obtained sets of volume data; setting
a plurality of analysis points in each of the extracted coronary artery
regions establishing correlations among the analysis points, which are
set at the same anatomical positions, within the plurality of coronary
artery regions; calculating index values that indicate the character of
plaque at each of the analysis points within all of the plurality of
coronary artery regions; evaluating the character of plaque at positions
within the coronary artery regions, by integrating the index values which
are calculated at the plurality of analysis points corresponding to each
of the positions; and outputting the evaluation results regarding the
character of plaque at each of the positions within the coronary artery
regions, correlated with information regarding the positions.

10. A coronary artery analyzing method to be performed by at least one
computer, comprising: obtaining a plurality of sets of volume data, each
of which represent the state of a beating heart in different phases;
extracting coronary artery regions from at least two sets of volume data
from among the obtained sets of volume data; setting a plurality of
analysis points in each of the extracted coronary artery regions
establishing correlations among the analysis points, which are set at the
same anatomical positions, within the plurality of coronary artery
regions; calculating index values that indicate the character of plaque
at each of the analysis points within all of the plurality of coronary
artery regions; evaluating the character of plaque at positions within
the coronary artery regions, by integrating the index values which are
calculated at the plurality of analysis points corresponding to each of
the positions; and outputting the evaluation results regarding the
character of plaque at each of the positions within the coronary artery
regions, correlated with information regarding the positions.

Description:

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention is related to a method and an apparatus for
assisting diagnosis by physicians by analyzing the states of coronary
arteries based on there dimensional data. The present invention is also
related to a recording medium in which a program that causes at least one
computer to execute the diagnosis assisting method is recorded.

[0003] 2. Description of the Related Art

[0004] Apparatuses and software programs that analyze the states of organs
and blood vessels based on three dimensional image data (volume data)
obtained by CT (Computed Tomography) examinations are being provided as
tools for assisting image diagnosis by physicians. Cardiac function
analyzing functions and coronary artery analyzing functions are widely
utilized as functions for assisting diagnosis of the heart. With respect
to the cardiac artery analyzing functions, Japanese Unexamined Patent
Publication No. 2009-195561 discloses an apparatus that extracts the
intravascular regions and soft plaque candidates from a single three
dimensional X ray CT image, and displays the extracted soft plaque
regions overlapped on the three dimensional CT image.

[0005] A plurality of sets of volume data that represents the state of the
heart at different points in time are necessary in order to understand
the movement of the heart (temporal changes) when analyzing cardiac
functions. For this reason, a plurality of sets of volume data having
different phases within a single cardiac cycle are generally obtained
during examinations of the heart. Meanwhile, the state of stenosis does
not change dramatically within a single cardiac cycle. For this reason,
analysis of coronary arteries is performed employing a single set of
volume data, as described in Japanese Unexamined Patent Publication No.
2009-195561.

[0006] It is desirable to employ a set of volume data which is obtained
when the movement of cardiac muscles is minimal when analyzing coronary
arteries. Sets of volume data which are obtained when the movement of
cardiac muscles is great often include problems, such as motion artifacts
and faulty contrast caused by shifts in the injection timing of contrast
agents, resulting in accurate extraction of coronary artery regions
becoming difficult. Commonly, it is considered that sets of volume data
obtained during a middiastolic state are favorable to be used for
analyzing coronary arteries.

[0007] However, there are differences in the shapes of hearts and the
movement of the cardiac muscles among individuals. In addition, the
beating of the heart is accompanied by twisting of the cardiac muscles.
Therefore, the movement of the cardiac muscles differs in the periphery
of the left coronary artery and in the periphery of the right coronary in
the same heart. For this reason, it is not always the case that a set of
volume data that represents the heart in a middiastolic state is optimal
as data to be utilized to analyze coronary arteries. In fact, there are
reports that sets of volume data obtained during telesystolic states are
favorable to analyze right coronary arteries and to analyze states during
high heart rates. Therefore, it is difficult to determine a single
optimal phase for analysis. Based on these circumstances, selection of
volume data (selection of an optimal phase) to be employed to analyze
coronary arteries is currently being performed based on visual
evaluations by physicians and technicians.

[0008] As described above, it had been conventionally necessary to select
one specific phase to perform coronary artery analysis. It is difficult
for a computer to automatically select an optimal phase, and it had been
necessary to rely on the visual evaluations of physicians and
technicians. There is a problem that accurate evaluation results cannot
be obtained if the selection of an optimal phase is erroneous. In view of
these circumstances, it is an object of the present invention to provide
an apparatus and a method which are capable of constantly accurately
analyzing and evaluating the stenosis state of coronary arteries. It is
another object of the present invention to provide a recording medium
having a program, that causes at least one computer to execute the method
of the present invention, stored therein.

[0009] A diagnosis assisting apparatus of the present invention is
equipped with a volume data obtaining means, a coronary artery region
extracting means, a correlation establishing means, an index value
calculating means, an index value integrating means, and an output
control means, as means for achieving the above objective. A coronary
artery analyzing program which is stored in a recording medium of the
present invention is a software program that causes one or a plurality of
computers to function as the volume data obtaining means, the coronary
artery region extracting means, the correlation establishing means, the
index value calculating means, the index value integrating means, and the
output control means.

[0010] The coronary artery analyzing program is generally constituted by a
plurality of program modules. The function of each of the means listed
above is performed by one or a plurality of the program modules. The
group of program modules is provided to users by being recorded in
storage media such as CD-ROM's and DVD's, by being recorded in a storage
unit attached to a server computer in a downloadable state, or by being
recorded in network storage (non transitory storage) in a downloadable
state. A coronary artery analyzing method of the present invention is a
method that analyzes the states of coronary arteries, by executing the
processes of the volume data obtaining means, the coronary artery region
extracting means, the correlation establishing means, the index value
calculating means, the index value integrating means, and the output
control means, which will be described below.

[0011] The volume data obtaining means obtains a plurality of sets of
volume data, each of which represent the state of a beating heart in
different phases. In the case that the obtained sets of volume data are
to be employed to analyze cardiac functions, it is preferable for the
volume data obtaining means to obtain volume data which are generated and
output by a modality, such as a CT apparatus, for all phases during a
single cardiac cycle. Meanwhile, in the case that the obtained sets of
volume data are to be employed only to analyze coronary artery functions,
it is not necessary to obtain volume data for all phases, and volume data
that represent phases within predetermined ranges may be obtained.

[0012] The coronary artery region extracting means extracts coronary
artery regions from at least two sets of volume data from among the sets
of volume data obtained by the volume data obtaining means. The coronary
artery region extracting means may perform extracting processes with
respect to all sets of volume data supplied by the volume data obtaining
means. Alternatively, the coronary artery region extracting means may
perform the extracting processes only with respect to sets of volume data
that represent the states of specific phases, from among the sets of
volume data supplied by the volume data obtaining means. It is preferable
for the process to extract the coronary artery regions to be executed
with respect to one or a plurality of sets of volume data that represents
the heart in a telesystolic state, and one or a plurality of sets of
volume data that represents the heart in a middiastolic state, from the
viewpoint of analysis accuracy.

[0013] The correlation establishing means sets a plurality of analysis
points in each of the extracted coronary artery regions, and establishes
correlations among the analysis points, which are set at the same
anatomical positions, within the plurality of coronary artery regions.
The points which are set as analysis points may be those which are
extracted by the coronary artery region extracting means as points that
represent the paths of the coronary arteries, may be selected from among
such points. Note that the expression "the same anatomical positions"
refers to positions within ranges which are recognized as the same
portions during diagnosis, and it is not necessary for the positions to
match completely.

[0014] The index value calculating means, calculates index values that
indicate the character of plaque at each of the analysis points within
all of the plurality of coronary artery regions. Here the expression
"character of plaque" refers to whether plaque is present, the percentage
occupied by plaque (stenosis rate), the properties of plaque (such as
whether the plaque is unstable), components of plaque, etc. The index
values the index value are calculated based on at least one of the
diameter, the area, and the signal values of either the coronary artery
region, the intravascular regions of coronary arteries, or both. A
plurality of index values will be calculated with respect to points which
are at the same anatomical positions by the processes executed by the
index value calculating means.

[0015] The index value integrating means evaluates the character of plaque
at positions within the coronary artery regions, by integrating the index
values which are calculated at the plurality of analysis points
corresponding to each of the positions. For example, the index value
integrating means may evaluate the character of plaque based on a total
sum of a plurality of index values. Alternatively, the index value
integrating means may calculate weighted averages of the plurality of
index values by multiplying the index values by weighting coefficients
which are set for each of the phases, and evaluate the character of
plaque based on the values of the weighted averages. The index value
integrating means evaluates the character of plaque based on a plurality
of index values. Therefore, the influence imparted by inaccurate values
to the evaluation results is lessened, even in cases that some of the
plurality of the index values are inaccurate.

[0016] In the case that the weighted averages of the index values are
calculated by the index value integrating means, it is preferable for the
weighting coefficients that the index values calculated for analysis
points corresponding to positions within a right coronary artery region
are multiplied by to be set higher for the telesystolic phase than for
other phases. Meanwhile, it is preferable for the weighting coefficients
that the index values calculated for analysis points corresponding to
positions within a left coronary artery region are multiplied by to be
set higher for the middiastolic phase than for other phases. By setting
the weighting coefficients in this manner, the influence imparted on the
evaluation results by index values calculated from sets of volume data
obtained during periods when the movement of the cardiac muscles is small
can be relatively increased. Thereby, the accuracy of evaluations can be
improved.

[0017] The output control means outputs the evaluation results regarding
the character of plaque at each of the positions within the coronary
artery regions, correlated with information regarding the positions.
Recording of the evaluation results into recording media, and output of
the evaluation results to a printer may be considered as manners in which
the evaluation results are output, in addition of display on a screen. In
the case that the evaluation results are displayed on a screen, it is
preferable for the evaluation results for each position to be displayed
such that they overlap images that represent the coronary artery region.

[0018] A configuration may be adopted, in which the diagnosis assisting
apparatus further comprises alert required region detecting means, for
detecting alert required regions based on the evaluation results
regarding the character of plaque in addition to the aforementioned
means. In this case, the output control means displays or prints the
detected alert required regions in a discernable manner during output of
the evaluation results. Here, the expression "alert required regions"
refers to regions at which careful observation is thought to be required
during diagnosis. In other words, the alert required regions are regions
which have possibilities of being factors that may cause serious
disorders. The alert required region detecting means may detect regions
having index values that indicate a stenosis rate greater than a
predetermined threshold value as alert required regions. Alternatively,
the alert required region detecting means may detect regions having index
values that indicate instability of plaque greater than a predetermined
threshold value as alert required regions. Further, the alert required
region detecting means may detect regions having index values that
indicate a stenosis rate greater than a predetermined threshold value and
index values that indicate instability of plaque greater than a
predetermined threshold value as alert required regions. If the alert
required region detecting means is provided, regions of interest can be
focused on in advance, to reduce the burden of observations placed on
physicians, thereby improving efficiency of diagnosis.

[0019] According to the present invention, it is not necessary to select
specific phases to perform analysis. Accordingly, the burden of selecting
phases is not placed on physicians or technicians. In addition, a
plurality of sets of volume data that represent states in different
phases are utilized to perform analysis. Therefore, the influence of sets
of volume data having poor image quality can be reduced. As a result,
great errors do not occur in the analysis results, and constantly uniform
accuracy in analysis can be guaranteed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020]FIG. 1 is a diagram that illustrates the schematic structure of a
diagnosis assisting apparatus according to an embodiment of the present
invention.

[0021]FIG. 2 is a diagram for explaining imaging synchronized with ECG's.

[0023]FIG. 3B is a diagram that illustrates a heart at a phase R-R10%.

[0024]FIG. 3C is a diagram that illustrates a heart at a phase R-R20%.

[0025]FIG. 3D is a diagram that illustrates a heart at a phase R-R30%.

[0026]FIG. 3E is a diagram that illustrates a heart at a phase R-R40%.

[0027]FIG. 3F is a diagram that illustrates a heart at a phase R-R50%.

[0028]FIG. 3G is a diagram that illustrates a heart at a phase R-R60%.

[0029]FIG. 3H is a diagram that illustrates a heart at a phase R-R70%.

[0030] FIG. 3I is a diagram that illustrates a heart at a phase R-R80%.

[0031]FIG. 3J is a diagram that illustrates a heart at a phase R-R90%.

[0032] FIG. 4 is a diagram that illustrates an example of a cardiac region
extracted by a coronary artery region extracting means.

[0033]FIG. 5 is a diagram that illustrates examples of candidate points
detected by the coronary artery region extracting means.

[0034]FIG. 6 is a diagram that illustrates an example of a tree structure
constructed by linking extracted candidate points.

[0035] FIG. 7 is a diagram that illustrates an example of a reference
coordinate system.

[0036]FIG. 8 is a diagram for explaining the differences in the paths of
coronary arteries during different phases.

[0037]FIG. 9 is a diagram that illustrates examples of set analysis
points.

[0038] FIG. 10 is a diagram for explaining a process performed by a
correlation establishing means.

[0039]FIG. 11A is a diagram that illustrates a cross section of a
coronary artery region without plaque.

[0040]FIG. 11B is a diagram that illustrates a cross section of a
coronary artery region with plaque.

[0041]FIG. 12 is a diagram for explaining a method by which regions are
judged.

[0042]FIG. 13 is a diagram that illustrates an example of a screen output
by an output control means.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0043] Hereinafter, embodiments of a diagnosis assisting apparatus, a
coronary artery analyzing method, and a recording medium in which a
coronary artery analyzing program is recorded of the present invention
will be described with reference to the attached drawings.

[0044]FIG. 1 illustrates the schematic structure of a hospital system 1
that includes a diagnosis assisting apparatus according to an embodiment
of the present invention. The hospital system 1 is constituted by: an
examination room system 3; a data server 4; and a diagnosis workstation 6
(WS 6); which are connected to each other via a local area network 2 (LAN
2).

[0045] The examination room system 3 is constituted by: various modalities
32 for imaging subjects; and an examination room workstation 31 (WS 31)
for confirming and adjusting images output from each modality. Examples
of the modalities 32 include: an X ray imaging apparatus; an MSCT (Multi
Slice Computed Tomography) apparatus; a DSCT (Dual Source Computed
Tomography) apparatus; an MRI (Magnetic Resonance Imaging) apparatus, and
a PET (Positron Emission Tomography) apparatus. The modalities 32 are
apparatuses that comply with DICOM (Digital Imaging and Communication in
Medicine) standards that appends data to the obtained sets of volume data
and outputs them as DICOM files.

[0046] The files output by the modalities 32 are forwarded to the data
server 4 by the examination room WS 31. The data server 4 is a
comparatively high processing performance computer equipped with a high
performance processor and a high capacity memory, in which a software
program that provides the functions of a DBMS (Database Management
Server) is installed. The software program is stored in the memory, and
executed by the processor. The data server 4 causes the volume data sent
from the examination room WS 31 to be stored in a high capacity storage
5. In addition, the data server selects files that satisfy search
conditions from among the plurality of files stored in the high capacity
storage 5, in response to search requests from the diagnosis WS 6. Then,
the data server 4 sends the selected files to the diagnosis WS 6.

[0047] The diagnosis WS 6 is a general purpose workstation equipped with a
normal processor, memory and storage, in which a diagnosis assisting
program is loaded. The diagnosis assisting program is installed in the
diagnosis WS 6 from a recording medium such as a DVD, or by being
downloaded from a server computer on a network. In addition, a display 7,
and input devices 8 such as a keyboard and a mouse are connected to the
diagnosis WS 6.

[0048] The diagnosis assisting program installed in the diagnosis WS 6 is
constituted by a group of program modules that realize various functions.
Among the program modules is a group of program modules that realizes
coronary artery analyzing functions. These programs are recorded in the
storage, loaded into the memory when booted up, and executed by the
processor. Thereby, the diagnosis WS 6 operates as various processing
means that include a volume data obtaining means 61, a coronary artery
region extracting means 62, a correlation establishing means 63, an index
value calculating means 64, an index value integrating means 65, an alert
required region detecting means 66, and an output control means 67
illustrated in FIG. 1.

[0049] Imaging synchronized with ECG's employing an MSCT apparatus or a
DSCT apparatus is performed for examinations of the heart. During imaging
synchronized with ECG's, 10 to 20 sets of volume data are obtained within
a single cardiac cycle, and output as files.

[0050] Hereinafter, imaging synchronized with ECG's will be described with
reference to FIG. 2. The upper portion of FIG. 2 represents the waveform
of an ECG. In the ECG, the period between a first R wave and a next R
wave corresponds to a single cardiac cycle. Positions along the
horizontal axis (temporal axis) of the ECG are phases, which are
represented as percentages by dividing the single cardiac cycle into 100.
For example, if 10 sets of volume data are obtained during the single
cardiac cycle at equal intervals, the phases represented by each set of
volume data are: R-R0%, R-R10%, R-R20%, R-R30%, R-R90%. Note that
although there are differences in heartbeats among individuals, in many
cases, the systolic phase is from R-R0% to about R-R45%, and the
diastolic phase is from about R-R45% to R-R100%.

[0051] FIGS. 3A through 3J are diagrams that illustrate examples of sets
of volume data obtained by imaging synchronized with an ECG. More
specifically, FIGS. 3A through 3J are examples of volume rendered images
(hereinafter, referred to as "VR images") which are generated from volume
data. FIG. 3A illustrates a heart at R-R0%, FIG. 3B illustrates a heart
at R-R10%, FIG. 3C illustrates a heart at R-R20%, FIG. 3D illustrates a
heart at R-R30%, FIG. 3E illustrates a heart at R-R40%, FIG. 3F
illustrates a heart at R-R50%, FIG. 3G illustrates a heart at R-R60%,
FIG. 3H illustrates a heart at R-R70%, FIG. 3I illustrates a heart at
R-R80%, and FIG. 3J illustrates a heart at R-R90%. FIGS. 3A through 3J
represent a heart as viewed from the front of a human body. The blood
vessel along the wall of the heart toward the left in the drawings is the
right coronary artery, and the blood vessel along the wall of the heart
toward the right in the drawings is the left coronary artery.

[0052] As is clear from the examples of FIGS. 3A through 3J, the ease with
which coronary artery regions can be discriminated within VR images
differ among the phases. In addition, the ease with which coronary artery
regions can be discriminated differ within a single VR image, according
to the positions and the thicknesses of the coronary artery. For example,
the tips of the coronary arteries are cut off and difficult to
discriminate in FIG. 3A (R-R10%) and FIG. 3J (R-R90%), but can be clearly
discriminated in FIG. 3G (R-R30%). In addition, the root portion of the
right coronary artery is clear in FIG. 3D (R-R30%). However, the boundary
between the coronary artery region and the cardiac muscle region is
unclear in FIG. 3F, and difficult to discriminate. For these reasons, it
is difficult to select a single phase which is optimal to discriminate
regions in.

[0053] Even assuming that a single optimal phase for discriminating
regions is selected from among the examples of FIGS. 3A through 3J, sets
of volume data of that phase will not always be optimal for
discriminating regions in. This is because there are differences in the
shapes of hearts and the movement of the cardiac muscles among
individuals, as described previously. Although FIGS. 3A through 3J are
merely examples, it is often the case that sets of volume data obtained
by imaging synchronized with ECG's have similar problems.

[0054] Hereinafter, the processes which are performed by each of the means
that constitute the diagnosis WS 6 will be described. If the function for
assisting diagnosis of coronary arteries is selected in an initial
screen, and the ID number of a patient or an examination number is input,
the volume data obtaining means 61 transmits the input data to the data
server 4, to request search and transfer files which are stored in the
high capacity storage 5. The files for which transfer is requested may be
files that represent specific phases. The range of phases to be requested
may be defined in setting data in advance, or may be specified by a user
by providing a predetermined user interface.

[0055] The data server 4 searches the files within the high capacity
storage 5 and transfers the requested group of files to the volume data
obtaining means 61 in response to the aforementioned request. If specific
phases are not specified in the request, the data server 4 transfers all
files which are obtained during a single cardiac cycle. On the other
hand, if specific phases are specified, the data server 4 transfers only
files that represent the specified phases. The volume data obtaining
means 61 stores the volume data included in the files transferred from
the data server 4 into the memory.

[0056] The coronary artery region extracting means 62 extracts coronary
artery regions, that is, the walls and the intravascular regions of the
coronary arteries, from each of the sets of volume data stored in the
memory by the above process. In addition, the paths of the coronary
arteries are specified during the process for extracting the coronary
artery regions. These processes may be performed with respect to all of
the supplied volume data. Alternatively, these processes may be performed
only with respect to sets of volume data that represent specific phases.
The targets of these processes (the range of phases to be processed) may
be defined in advance in setting data, or may be specified by a user by
providing a predetermined user interface.

[0057] In cases that processing efficiency is prioritized, it is
preferable for the number of sets of data to be processed to be reduced,
by the processing targets being narrowed by the volume data obtaining
means 61 or by coronary artery region extracting means 62 as described
above. The number of sets of data may be reduced by thinning 20 sets of
data are obtained at phase intervals of 5% by selecting 10 sets of volume
data at phase intervals of 10%, for example. Alternatively, sets of
volume data which are effective for analysis, such as those obtained
during a telesystolic phase and a middiastolic phase may be selected,
while sets of volume data that represent other phases are excluded.

[0058] Hereinafter, the process for extracting the coronary artery regions
will be described further. Various methods for extracting coronary artery
regions from volume data have been proposed. An example of such a method
is that disclosed in A. Szymczak et al., "Coronary Vessel Trees from 3D
Imagery: A Topological Approach", Medical Image Analysis, Vol. 10, Issue
4, pp. 548-559, 2006. Any known method can be applied to extract the
coronary artery regions. However, the present embodiment employs the
method proposed by the present applicant in Japanese Patent Application
Nos. 2009-048679 and 2009-069895. The outline of the process described in
these documents will be described hereinbelow.

[0059] The coronary artery region extracting means 62 extracts a region
corresponding to the heart (hereinafter, referred to as a "cardiac
region") from volume data based on a predetermined algorithm. FIG. 4 is a
diagram that illustrates an example of a cardiac region 9 extracted by
the coronary artery region extracting means 62. Positions Sref of points
that characterize the shape of the heart, such as the position of the
aortic valve, the position of the mitral valve, and the position of the
apex of the heart, are also specified during the process for extracting
the cardiac region 9. The coordinates of the specified positions are
stored in the memory, and utilized to define a reference coordinate
system in processes to be described later.

[0060] Next, a rectangular parallelepiped region that includes the cardiac
region 9 is set as a search range, linear structures are searched for
within the search range based on a predetermined algorithm. Further,
points which are estimated to be points along the cores of coronary
arteries are detected, based on the linear structures detected by the
search. In the following description, the points which are estimated to
be points along the cores of coronary arteries will be referred to as
candidate points or nodes. FIG. 5 illustrates an example of a linear
structure 10 and detected candidate points Ni.

[0061] The search for the linear structures is performed by calculating
eigenvalues of a 3×3 Hessian matrix for each local region within
the search range. In regions that include linear structures, one of the
three eigenvalues of the Hessian matrix becomes a value close to zero,
while the other two values will be relatively greater values. In
addition, the eigenvector that corresponds to the eigenvalue close to
zero indicates the direction of the main axis of the linear structures.
The coronary artery region extracting means 62 utilizes this relationship
to judge likelihoods of being linear structures based on the eigenvalues
of a Hessian matrix for each local region. In local regions in which
linear structures are discriminated, the center points thereof are
detected as candidate points.

[0062] Note that it is preferable for the resolution of the data within
the search range to be converted to generate a plurality of sets of data
having different resolutions (a Gaussian pyramid), and to repeatedly
perform searches (scans) at different resolutions. In the search method
described above, it is not possible to discriminate linear structures in
cases that the diameters (widths) of local regions are smaller than the
diameters of blood vessels. However, it will become possible to
discriminate linear structures of various sizes by performing searches in
different resolutions. Thereby, candidate points can be thoroughly
detected for thick blood vessels at the root portions and thin blood
vessels at the tips.

[0063] Next, the candidate points which are detected by the search are
linked based on a predetermined algorithm. Thereby, tree structures
constituted by the candidate points and blood vessel branches (edges)
that connect the candidate points are constructed, as illustrated in FIG.
6. The coordinate data of the detected plurality of candidate points and
vector data that represent the directions of the blood vessel branches
are stored in the memory, along with identifiers for the candidate points
and the blood vessel branches.

[0064] Next, the shapes of the coronary arteries are discriminated in
detail based on the values of the surrounding voxels (CT values) for each
detected candidate point. More specifically, the outlines (the outer
walls of the blood vessels) of the coronary arteries are discriminated
within cross sections perpendicular to the pathways of the coronary
arteries. The discrimination of shapes is performed employing a known
segmentation method, such as the Graph Cuts method.

[0065] Finally, the coronary artery region extracting means 62 defines a
reference coordinate system, in which the positions Sref of the aortic
valve, the mitral valve, and the apex of the heart which were stored in
the process for discriminating the cardiac region 9 are designated as
reference positions. For example, the apex of the heart is set as the
origin of the reference coordinate system, the direction from the apex of
the heart toward the aortic valve is designated as a Z axis, and the X
and Y axes are defined based on the relationship with the mitral valve,
as illustrated in FIG. 7. In addition, the scale of the coordinate system
is normalized by defining the distance from the apex of the heart to the
aortic valve as 1. Then, the coordinate values which were recorded in the
memory by the aforementioned processes are converted to coordinate values
within the reference coordinate system. That is, data that represent the
positions of the candidate points and the branches, the outlines of the
coronary arteries, and the like are normalized. The normalized data are
correlated with data prior to normalization, and stored in the memory.
The normalized data regarding the candidate points and the outlines will
be referred to as coronary artery region data in the following
description.

[0066] Next, the processes which are performed by the correlation
establishing means 63 will be described. As described above, the
processes which are performed by the coronary artery region extracting
means 63 are performed with respect to a plurality of sets of volume data
which are obtained during different phases. Accordingly, a plurality of
sets of coronary artery region data are obtained for coronary arteries
for a single heart.

[0067] The positions and shapes of the coronary arteries change
accompanying the beating of the heart. Therefore, the candidate points
which are detected within sets of volume data that represent different
phases will not always have the same positional coordinates, even if they
are positioned at the same anatomical points. For example, FIG. 8 is a
diagram in which the main portions of the coronary artery regions in FIG.
3D (R-R30%) and FIG. 3H (R-R70%) are overlapped on each other. As
illustrated in FIG. 8, the paths of the coronary arteries differ during
the systolic phase and the diastolic phase.

[0068] The correlation establishing means 63 establishes correlations
among the plurality of sets of coronary artery region data obtained with
respect to the coronary arteries of a single heart. More specifically,
correlations are established among candidate points which have different
positional coordinates within the reference coordinate system but are
positioned at the same anatomical points. Correlations may be established
among all of the candidate points that constitute the tree structures.
However, in the present embodiment, a portion of the candidate points are
set as analysis points as illustrated in FIG. 9, and correlations are
established only among the set analysis points. The analysis points are
set by the following process.

[0069] The correlation establishing means 63 divides the tree structures
which are specified by the candidate points and the branches into
segments. In the present embodiment, candidate points which are linked to
three or more branches, that is, candidate points which are positioned at
branching points, are set as the boundaries of the segments. Further,
candidate points and branches that extend beyond the branching points are
divided into segments having a predetermined number of candidate points
or segments having a predetermined length. Then, the candidate points
which are positioned at the boundaries of each segment are selected as
the analysis points. The analysis points are set by the correlation
establishing means 63 storing data necessary to specify the analysis
points (the positional coordinates or identifiers of the candidate
points).

[0070] After the analysis points are set, the correlation establishing
means 63 establishes correlations among analysis points which are
estimated to be at the same anatomical points by a graph matching
technique. In the present embodiment, the correlation establishing means
63 calculates degrees of similarity among analysis points which are set
along the paths of coronary arteries based on a predetermined evaluating
function, and establishes correlations among analysis points having the
highest degrees of similarity. The evaluating function is defined, taking
the positional coordinates within the reference coordinate system, the
numbers and coordinate values of the candidate points which are linked to
the analysis points, the diameters of the blood vessels in the periphery
of the analysis points, etc., into consideration. At this time, it is
preferable for the number and the types of elements to be considered to
be set while considering a balance between the accuracy of evaluations
and processing time. According to this technique, analysis points which
are positioned at the same anatomical points can be correlated with each
other even if the shapes and the positions of coronary arteries differ
during the systolic phase and the diastolic phase, as illustrated in FIG.
10.

[0071] Note that various methods have been proposed with respect to
establishing correlations among anatomical structures by a graph matching
method, as exemplified in U.S. Pat. No. 7,646,903. Other known techniques
may be employed to establish correlations among the analysis points.

[0072] Next, the processes which are performed by the index value
calculating means 64 will be described. The index value calculating means
64 calculates an index value that indicates the character of plaque at
each analysis point within each of the plurality of coronary artery
regions extracted by the coronary artery region extracting means 62. FIG.
11A and FIG. 11B are diagrams that illustrate cross sections of coronary
arteries at analysis points, wherein FIG. 11A illustrates a normal
coronary artery, and FIG. 11B illustrates a coronary artery having plaque
deposits on the inner walls thereof.

[0073] First, the index value calculating means 61 discriminates an
intravascular region 12 and a plaque region 13 within a coronary artery
region 11. Generally, the CT values of soft plaque are lower than the CT
values of a normal intravascular region, and the CT values of hard plaque
are higher than the CT values of a normal intravascular region. In MRI's
as well, it is known that signal values for plaque are outside the range
of signal values for normal intravascular regions. Therefore, the index
value calculating means 64 utilizes this relationship among signal values
to distinguish plaque regions and intravascular regions. More
specifically, the value of each voxel that constitutes cross sections is
compared against predetermined threshold values, to judge whether the
voxels represent plaque or intravascular regions. A region constituted by
voxels which have been judged to represent plaque is designated as the
plaque region 13, and a region constituted by voxels which have been
judged to represent an intravascular region is designated as the
intravascular region 12. Note that plaque is also classified into soft
plaque and hard plaque.

[0074] Here, the range of possible signal values that represent
intravascular regions depends on the thickness of blood vessels and
imaging conditions, and therefore is not constant. For this reason, it is
preferable for the threshold values which are employed to distinguish
plaque regions and intravascular regions to be values that change
according to the thicknesses of blood vessels. In the present embodiment,
as illustrated in FIG. 12, the threshold values are set as two
borderlines B1 and B2 that divide the plane of a coordinate having signal
values as the horizontal axis and the diameters of blood vessels (average
diameters or average radii measured in a plurality of directions) as the
horizontal axis into three sections. These borderlines are set by
performing learning in advance, using sample data that represent normal
blood vessels and blood vessels with plaque deposits having different
thicknesses. The set borderlines are stored in the memory, and referred
to by the index value calculating means 64.

[0075] The index value calculating means 64 judges whether voxels
represent soft plaque, hard plaque, or an intravascular region, based on
which sides of the borderlines B1 and B2 coordinate points (signal values
of the voxels and the diameter of blood vessels) on the coordinate plane
of FIG. 12 are positioned. The thicknesses of blood vessels are in a
correlative relationship not only with the diameters of blood vessels,
but also with the areas thereof. Therefore, the vertical axis of the
coordinate plane for setting the borderlines may alternatively be the
areas of blood vessels.

[0076] Next, the index value calculating means 64 calculates two index
values that indicate the character of plaque. A first index value I1 is
the percentage of the coronary artery region that plaque occupies, that
is, the stenosis rate. The first index value I1 is calculated by Formula
(1) below. In Formula (1), Aplaque is the area of the plaque region 13,
and Aall is the area of the coronary artery region 11. Alternatively,
Aall may be a combined area of the plaque region 13 and the intravascular
region 12. Note that the area of each of the regions can be derived based
on the number of voxels that constitute the region.

I1=Aplaque/Aall100 (1)

[0077] A second index value I2 is a value that represents the likelihood
that plaque is unstable. Among plaque which adheres to the walls of blood
vessels, soft plaque is more unstable compared to hard plaque. Therefore,
it is said that the risk of thrombosis occurring due to plaque breakdown
is more likely to occur with soft plaque compared to hard plaque. The
index value I2 becomes high in cases that the signal values of plaque
regions are low (soft plaque) and becomes low in cases that the signal
values of plaque regions are high (hard plaque). For example, a value of
1 is output as the index value I2 if the average signal value of a plaque
region is positioned toward the left of the borderline B1; a value of 0.5
is output as the index value I2 if the average signal value of a plaque
region is positioned toward the right of the borderline B2; and a value
of 0 is output if no plaque regions are present.

[0078] In addition, the index value calculating means 64 may output a
value obtained by multiplying the index value I1 by the index value I2 as
a third index value I3. Note that the index values which are calculated
by the index value calculating means 64 are not limited to the examples
described above. Other examples of index values include index values that
indicate judgment results regarding whether plaque is present, index
values that indicate characteristics other than instability (for example,
hardness), and index values that indicate the components of plaque. In
addition, in the example described above, the index values are calculated
based on the diameters or the areas of coronary artery regions and the
signal values of voxels. However, there are index values that can be
calculated based only on the diameters, only on the areas, and only on
the signal values. Ratios between the diameters or the areas of coronary
artery regions and intravascular regions may be calculated as index
values that indicate the degree of stenosis, for example. Alternatively,
whether plaque is present may be judged based only on signal values, and
the judgment results may be output as index values.

[0079] By executing the processes described above for each of the analysis
points, index values I1 and I2 are obtained for each of the analysis
points for which correlations have been established by the correlation
establishing means. For example, in the case that the coronary artery
region extracting means 62 executes the coronary artery region extracting
process for six phases, 6n values are calculated for each type of index
value, as illustrated in Table 1. Although Table 1 illustrates
calculation results for index value I1, 6n values are also obtained for
index value I2.

[0080] Note that in Table 1 and the following description, the plurality
of analysis points which are set along the paths of coronary arteries in
phases R-Rx % (0≦x<100, x represents a phase) and correlated
with each other are expressed as apxi (0<i≦n, i represents an
identifier of each analysis point, and n represents the number of
analysis points). In addition, points which are correlated and recognized
as being points at the same anatomical position are expressed as APi. The
point APi represents a single anatomical point, but is a group of a
plurality of pieces of data, and APi={ap00i, ap10i, . . . , ap80i,
ap90i}.

[0081] Next, the processes which are performed by the index value
integrating means 65 will be described. The index value calculating means
64 performs the index value calculating processes with respect to the
analysis points apxi, which are set within each of the coronary artery
regions. For this reason, the index value calculating means 64 calculates
a plurality of index values for points (cross sections) which are
positioned at the same anatomical points. The index value integrating
means 65 derives a single integrated evaluation value for each analysis
point APi, by integrating the plurality of index values which are output
by the index value calculating means 64.

[0082] In the present embodiment, the index value integrating means 65
calculates weighted averages of the plurality of index values I1 and I2
by obtaining weighted averages of the index values which have been
calculated for each phase, and outputs the weighted averages as
evaluation values. Here, evaluation values which are derived from the
index values I1 will be expressed as V1, and evaluation values which are
derived from the index values I2 will be expressed as V2. Evaluation
values V1APi for the points APi can be calculated by Formula (2) below.

V 1 APi = x α x × I 1 xi
( 2 ) ##EQU00001##

[0083] wherein αx is a weighting coefficient set for a phase
R-Rx %, and I1xi (0<i≦n) is each of the index values I1
calculated for analysis points apxi along the path of the coronary
arteries in the phase R-Rx %.

[0084] Values of the weighting coefficients αx for all possible
phases are registered in the memory in advance. In the present
embodiment, combinations of a plurality of weighting coefficients are
registered, and the weighting coefficients can be switched by a user
performing a selecting operation. Table 2 illustrates an example of
settings of weighting coefficients αx. Note that the numerical
values shown in Table 2 are merely examples.

[0085] In Table 2, setting example S1 sets the weighting of phases other
than the telesystolic phase and the middiastolic phase to 0, and places
high weighting coefficients with respect to phases R-R40% and R-R70%. The
index values calculated for phases in which the influence of motion
artifacts, etc. are less likely to occur are heavily weighted, while the
index values calculated for the other phases are lightly weighted.
Thereby, the reliability of the calculated evaluation values can be
improved.

[0086] In addition, in Table 2, setting example S2A enables obtainment of
evaluation values only from index values calculated for the telesystolic
phase (R-R70% and the vicinity thereof), while setting example S2B
enables obtainment of evaluation values only from index values calculated
for the middiastolic phase (R-R40% and the vicinity thereof). Using these
examples, the index values which are calculated for analysis points set
within the left coronary artery region may be multiplied by the weighting
coefficients of setting example S2A, and the index values which are
calculated for analysis points set within the right coronary artery
region may be multiplied by the weighting coefficients of setting example
S2B. Thereby, the reliability of the calculated evaluation values can be
further improved.

[0087] The alert required region detecting means 66 detects regions at
which alerts are required, based on the evaluation values V1APi and V2APi
calculated by the index value integrating means 65. In the present
embodiment, if at least one of the evaluation values V1APi and V2APi
calculated for an analysis point APi is greater than threshold values
stored in a memory, the analysis point APi is detected as an alert
required region. That is, regions at which the stenosis rate is high are
judged as alert required regions even if plaque is stable, and regions at
which the instability of plaque is high are judged as alert required
regions even if the stenosis rate is low. It goes without saying that
regions having both high stenosis rates and high instability of plaque
are judges as alert required regions. Note that it is preferable for the
threshold values to be employed to detect the alert required regions to
be determined based on past cases. In addition, the definitions of and
the detecting methods for the alert required regions may be appropriately
determined according to diagnosis principles, and are not limited to
those described above.

[0088] The output control means 67 outputs the evaluation values of the
analysis points and data regarding the alert required regions to the
screen of the display 7. FIG. 13 is a diagram that illustrates an example
of an output display screen. In the present embodiment, an image of the
coronary artery region, which is color coded according to the evaluation
values regarding the character of plaque, is generated and displayed on
the screen. The image of the coronary artery region to be displayed may
be a volume rendered image or a CPR image. Further, a highlighting color
such as red is assigned to a region which is judged to be an alert
required region, and an arrow 14 that points to the alert required region
is also displayed. However, the manner in which the evaluation results
and the alert required regions are indicated is not limited to the
example illustrated in FIG. 13, and it goes without saying that various
modifications are possible.

[0089] Note that the output control means 67 may output an image of the
display screen, a list of numerical values that indicates the
correlations among identifiers of the analysis points and the evaluation
values, etc. to a printer or to a recording medium, in addition to
outputting the display screen to the display 7.

[0090] It is rare for the character of plaque to change suddenly within a
single cardiac cycle. Therefore, the index values that represent the
character of plaque should fundamentally be approximately equal, as in
point APj of Table 1. However, there are cases in which coronary artery
regions cannot be accurately detected from volume data due to poor image
quality, and erroneous calculation results are obtained for a portion of
the phases, as in point APk of Table 1.

[0091] In a conventional method, in the case that the phase R-R50% is
selected, the character of plaque cannot be evaluated correctly. In
contrast, the present embodiment evaluates the character of plaque based
on a plurality of index values which are calculated for a plurality of
phases. Therefore, even if a portion of the index values are erroneous,
correct evaluation results can be obtained as a whole. In addition, a
conventional method requires physicians to select a set of volume data to
be utilized for analysis while observing VR images such as those
illustrated in FIGS. 3A through 3H. However, it is not necessary to
select a single set of data in the method of the present embodiment, and
no burden is placed on physicians.

[0092] Note that in the embodiment described above, the alert required
region detecting means 66 specifies alert required regions in order to
lessen the burden placed on physicians during diagnosis. However, the
diagnosis WS 6 may simply display the evaluation results regarding the
character of plaque, and leave specification of alert required regions to
a physician's judgment. That is, an embodiment may be considered in which
the diagnosis WS 6 is not equipped with the alert required region
detecting means 66.

[0093] In addition, the character of plaque is evaluated by calculating
the weighted averages of the index values in the embodiment described
above. However, embodiments may be considered in which the character of
plaque is evaluated by other evaluating methods, such as calculating
simple averages and sums of the plurality of index values. The number of
sets of volume data which are obtained, the method for selecting sets of
volume data prior to extracting the coronary artery regions, the
weighting coefficients, etc. may be changed as appropriate according to
the specifications of the modality, the examination method, and the
symptoms of subjects.

[0094] Further, the all of the series of processes from the obtainment of
the sets of volume data to output control is performed by the diagnosis
WS 6 in the embodiment described above. Alternatively, the series of
processes may be divided among and executed by a plurality of computers.

[0095] As described above, the present invention is not limited to the
embodiment described above. Various changes and modifications are
possible, as long as they do not stray form the spirit and scope of the
invention.